The present invention relates to resource management in distributed computer networks. More specifically, but without limitation thereto, the present invention relates to a scheduling framework for assigning a set of problems to computers in a heterogeneous computer network to achieve a substantially optimal matching of the problems to the computers to minimize the amount of time required to solve the problems.
In recent years networks of distributed computers have emerged to support high performance computing as an alternative to various monolithic parallel machines such as Cray and Paragon. Two approaches based on workstations are generally referred to as COW's (clusters of workstations) and NOW's (network of workstations) . Another approach called heterogeneous computing attempts to combine the best of the cluster and network approaches by efficient use of diverse machines. This technique may offer great potential but requires intelligent orchestration of the tasks to match the resources. Distributed computing environments (DCE's) and resource management systems (RMS's) have primarily arisen from the NOW/COW arena. DCE's such as PVM (parallel virtual machines) and CRONUS (an object-oriented distributed computing environment commercially available for the development of large-scale distributed heterogeneous applications) and resource management systems (RMS's) such as Condor and LoadLeveler can carry out a distributed policy but do not necessarily create an intelligent policy or schedule. Most of these DCE's and RMS's rely on load-balancing (next task to the next available machine) which increases the usage of the machines, but does not necessarily minimize the total time to solve a problem.
A continuing need therefore exists for an intelligent scheduling framework for heterogeneous computer networks that can minimize the throughput time required for solving a set of computational tasks.